Deep learning is widely applicable to phenotyping embryonic development and disease.
Animals
Craniofacial Abnormalities
/ embryology
Deep Learning
Disease Models, Animal
Embryonic Development
/ genetics
Image Processing, Computer-Assisted
Mice
Microscopy
Mutation
Neural Networks, Computer
Neurodevelopmental Disorders
/ genetics
Phenotype
Polycystic Kidney Diseases
/ embryology
Xenopus Proteins
/ genetics
Xenopus laevis
Xenopus
Craniofacial dysmorphia
Cystic kidney disease
Deep learning
Light-sheet microscopy
U-Net
Journal
Development (Cambridge, England)
ISSN: 1477-9129
Titre abrégé: Development
Pays: England
ID NLM: 8701744
Informations de publication
Date de publication:
01 11 2021
01 11 2021
Historique:
received:
30
03
2021
accepted:
24
09
2021
entrez:
5
11
2021
pubmed:
6
11
2021
medline:
17
12
2021
Statut:
ppublish
Résumé
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated 'The people behind the papers' interview.
Identifiants
pubmed: 34739029
pii: 273338
doi: 10.1242/dev.199664
pmc: PMC8602947
pii:
doi:
Substances chimiques
Xenopus Proteins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIMH NIH HHS
ID : U01 MH115747
Pays : United States
Organisme : NIH HHS
ID : P40 OD010997
Pays : United States
Organisme : NIDDK NIH HHS
ID : RC2 DK122397
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD084409
Pays : United States
Organisme : NIH HHS
ID : R24 OD030008
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK076683
Pays : United States
Informations de copyright
© 2021. Published by The Company of Biologists Ltd.
Déclaration de conflit d'intérêts
Competing interests The authors declare no competing or financial interests.
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